{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in First-Tier Cities 程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and city in ('北京','上海','广州','深圳')  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_description</th>\n",
       "      <th>...</th>\n",
       "      <th>bd_flink</th>\n",
       "      <th>bd_presto</th>\n",
       "      <th>bd_heron</th>\n",
       "      <th>ml_sklearn</th>\n",
       "      <th>ml_mllib</th>\n",
       "      <th>ml_tensorlayer</th>\n",
       "      <th>ml_paddlepaddle</th>\n",
       "      <th>ml_cntk</th>\n",
       "      <th>ml_theano</th>\n",
       "      <th>ml_mahout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22976</th>\n",
       "      <td>120372004</td>\n",
       "      <td>9000</td>\n",
       "      <td>280</td>\n",
       "      <td>软件开发、数据分析、产品、设计类岗位</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>中银金融科技有限公司（以下简称“中银金科”）于2019年6月11日由软件中心（上海）转制成立...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11348</th>\n",
       "      <td>116717167</td>\n",
       "      <td>10000</td>\n",
       "      <td>200</td>\n",
       "      <td>海外业务软件工程师（2020届）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7868</th>\n",
       "      <td>114568087</td>\n",
       "      <td>3750</td>\n",
       "      <td>151</td>\n",
       "      <td>C#软件开发工程师（雄安新区岗位）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24832</th>\n",
       "      <td>120749109</td>\n",
       "      <td>12500</td>\n",
       "      <td>150</td>\n",
       "      <td>2020春季应届生开发岗位</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>龙盈智达（深圳）科技有限公司是华夏银行旗下从事金融科技服务的全资科技公司。公司成立于2018...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24562</th>\n",
       "      <td>120688380</td>\n",
       "      <td>9000</td>\n",
       "      <td>100</td>\n",
       "      <td>软件开发、数据分析、产品、设计类岗位</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>中银金融科技有限公司诚聘</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_id  monthly_salary  headcount               title zhinengleibie  \\\n",
       "22976  120372004            9000        280  软件开发、数据分析、产品、设计类岗位         软件工程师   \n",
       "11348  116717167           10000        200    海外业务软件工程师（2020届）         软件工程师   \n",
       "7868   114568087            3750        151   C#软件开发工程师（雄安新区岗位）         软件工程师   \n",
       "24832  120749109           12500        150       2020春季应届生开发岗位         软件工程师   \n",
       "24562  120688380            9000        100  软件开发、数据分析、产品、设计类岗位         软件工程师   \n",
       "\n",
       "      career real_province province city  \\\n",
       "22976   数据分析          None       上海   上海   \n",
       "11348  一般程序员          None       上海   上海   \n",
       "7868   一般程序员          None       北京   北京   \n",
       "24832  一般程序员          None       北京   北京   \n",
       "24562   数据分析          None       上海   上海   \n",
       "\n",
       "                                     company_description  ... bd_flink  \\\n",
       "22976  中银金融科技有限公司（以下简称“中银金科”）于2019年6月11日由软件中心（上海）转制成立...  ...    False   \n",
       "11348  上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...  ...    False   \n",
       "7868   北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...  ...    False   \n",
       "24832  龙盈智达（深圳）科技有限公司是华夏银行旗下从事金融科技服务的全资科技公司。公司成立于2018...  ...    False   \n",
       "24562                                       中银金融科技有限公司诚聘  ...    False   \n",
       "\n",
       "      bd_presto bd_heron ml_sklearn  ml_mllib  ml_tensorlayer  \\\n",
       "22976     False    False      False     False           False   \n",
       "11348     False    False      False     False           False   \n",
       "7868      False    False      False     False           False   \n",
       "24832     False    False      False     False           False   \n",
       "24562     False    False      False     False           False   \n",
       "\n",
       "       ml_paddlepaddle  ml_cntk  ml_theano  ml_mahout  \n",
       "22976            False    False      False      False  \n",
       "11348            False    False      False      False  \n",
       "7868             False    False      False      False  \n",
       "24832            False    False      False      False  \n",
       "24562            False    False      False      False  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30429, 122)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97041"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6250., 11500., 15000., 20500., 40000.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月中国一线城市程序员的平均工资为17062元，工资中位数为15000元，其中95%的人的工资位于6250到40000元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国一线城市程序员的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 17062 Yuan as average, the median is 15000 Yuan, 95% of them earn between 6250 and 40000 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=9267.906811357438, pvalue=0.0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YuPtowbSYOGoQfzxvCZdPHsGM00vZsuejlL+/afcBvrVgJbdWj+Xac0bz8AsN7eYZf2J/zhkzmCsffJHJowcxefQgCsyoLBvKlQ++SGXZEOZcMp6Pmg/zb0sa+ObFp3HPwje475pJabc3ig+272P6qSW8un4n08tPYPWGXWnnHda/D9+fOZmvPLw07YPUsP59+NcX3+eFd7byyI3TOKG4NwBjhh7PtT9awjcuGkfV2KGs/bCJjbsO8LVHlrHglvP489rEg1T/PoUUHGdsbTqYNsdtNeN4+tUN/HzFBv7pqolccGoJAL99awv/vryRH984jTOGD+D1jbvb/W55aTFHHK790RJmnD6Mfr0L0q5nyuhBzH+hgX/45ZtA4sHrpk+fwrZ3Gpl2yhDmv9DAn0w7ibc37+H+R9fww+umMv7E/jy5vJFvf/4Mnl65kenlJTxY171nUPLf1yfqJZqWE2Ubdh44Wu5RrG5MlM2bG/cwanARO/cdYkH9en584zSunjqaQ83NaX/3S1NG8tQrjR2OHTh0mN6Fx/HUrefx1Iqun8z7xYoNNB9xNuzc3267Cgs/Lo4VH+w8uj0nDe2XclljS4pZuT6xvSvW7eTJ5esZN6yYV9cnfveVD3YyblgxAOt37OeIO+t37Mc9/fZ2pLAgke+1xt1cOnE4z7+5hc9NGH50n6dyfdUYNu46wMgUr3X3TW5/8xFn5rTR3D9zMoOO70XfXon1tL0NbNp1gAkjB/LE16uY/8J7R5czc9pJvL72gw6zlw8rPvoy1Ip1H++XY/fz8Sl/d3Xjbt7evIcf3ziN6aeWsP/QsQ9UfVtdj29t3sOvXtt09PJ7W/dy4sAiehUWsnv/IfYfOkxZSTGfPfNEamdXctKQIk4c0JcPtu+juE8hlWVDeGvzHj5qPtLh9kh4PlEFv//QsUV84NDho3f8jsYmjU685HHmiAG8v20fwwf2Zce+g1z/8Mts2n2AsaOHp1xf74LjGD6wLw3b9nU4Nmn0IJ57bTNfnPsi/2/xe6kW1SrfkXb52pbDweYjDOmXOGI9eXjp0fGzRg4EYPzwAazfsY9U1n7YxITkfFNPHsw/XzOZNVv2MCn5ss+Ukwbx9ubUJ4ZTbVsqx+YbBsBrG3Zx3tih/OGdrUw/tYTVje2Pels88Ns13PXUar558antlnfBaYmj6GsqRrNo1Ua+8fgr7Gt1lN/2NnDBaSU88Ns1XPHgi/x8xQYAhvTrTfORI3x08FCH2/H25j1MGT0YOHa/RNnPZwwfwLKGHVz/8MsMLOrFOWOGpNwO4Jj8LV5dt5PJ48v49RtbAHj3wyYe/sN7zJy3hO8+9zaNO/cD8PSrG/jOlZP49+U6b/BJ9Ikq+LYWr9nKH515IgturmLaKUPSjk0cNYja2ZUMKOrFb97YzId7PuKi8aUsuLmK88YOZd2mD48u84fXTT3689UVo9qdkEs1tnZLE396/ik8flMlD153NueMGZw282sbdjG2pB9PfL2KyyamfmB5ae02Ljp9GHd/4UzsODs6fsoJ/aidXcn5407gpy+va5cX4M1Ne1j+wQ4W3FzFX332NL7/mzW8vbmJJe9u48lbzuP2C8v5zrNvpVxvqm1r0aewgIPJI8hfv7GZr5w3hnu+eBYHDiZeAlm9YTeNO/bzwfZ9bGs6eLSgUvmo+Qgbdx1g7YdNzDh92DHL27EvUcp/eGcrt1aP47GbKgEoHdA35bJea9zN/7z8TB676Vwe+OMpnFpazMxzRvPTNG/v7NtqO+bWreWyScP52c1V7N7fzOI1WwH43IThLLi5inXb96V9oFq/Yx9f/dQYnrzlPEr692HV+l0ptyOdhas2Mnn8WH7zxmYAHn95HRecVsJPv17JrHNPYmPyxPCi1RtxnKUNOzpcnoTJvOW5dbZWYPYQcAaw0N3/Pt18FRUVvmzZsrTLqauro7q6OuW0bL5/tfUJwXTunNDMfavan84Y2q832/Ye7HSs5rRh3HxBGQcPH+HAoSP8YkVjxk6GpcuWDam2rVeB8cTXqzji8I/PvsnL7x27H3OZL5WZ54zm8skjaD7sHDp8hP+7+F3WbG5i296Dx2QbNbiIH8ycwmF35jy5irUfNuUtc4vO9l35sGL+6epJPPZfH/DEstx+HiHf12tn4pav9fvgO+q6VMys3t0rUk3L6haa2RVAgbtXmdnDZlbu7ms6/cUYaX0ys6vall26seff2sLzb23p9nriItW2HTrsfGnui3lIE03t0nWRPoy1fsd+rniwe9tRO7vymMt7Dhziph/Xp5k7c9ZsaeKL//JC1tcj8ZXVI3gz+wHwrLsvMrOZQJG7z281fTYwO3nxNCD1c/+EE4CtWQvbM8rWfXHOF+dsEO98cc4G8c7X1Wwnu3tJqgnZfo7SD2h5S8V24OzWE919HjAvyoLMbFm6pyH5pmzdF+d8cc4G8c4X52wQ73yZzJbtk6xNQMt72YpzsD4REUnKduHWA+cnf54ENGR5fSIikpTtl2ieAhab2QjgEqCyk/k7EumlnDxRtu6Lc744Z4N454tzNoh3voxly8XbJAcDFwO/d/dNnc0vIiKZkfWCFxGR/NBJTxGJxMyGmNnFZnZCvrO0Feds+RT7gjezh8zsJTO7Kw/rLjSzD8ysLvlvgpn9LzNbamb/0mq+SGMZzlZqZouTP/cys6fN7AUzu7GnYxnONtLM1rfahyXJ8XbXa9SxHuQaaGa/NLPnzOw/zKx3T3Jk+raZJt8xt7/kfDm/DSZfan0GmAY8b2Ylcdl3abLFYr+1yVlqZq8kf87Jvot1wVurT8ICZWZWnuMIE4HH3b3a3auB3iTeFTQN2GJmM8xsapSxTIZK3qAfIfE5A4DbgXp3/xRwlZn17+FYJrOdC9zTsg/d/cNU12vUsZ5kA2YB33P3zwCbgJndzZGl22bbfHNodftz91VRb29ZuA1OBL7p7vcAvwIuJD77rm22G4nPfmvtu0BRT/ZTV/ddrAseqAaeSP78HB+/5TJXKoHPm9nLlvibOhcBT3rixMWvgE8DF0Qcy6TDwLVAy1+yqubj/fR7oKKHY5nMVgl8zcyWm9n/SZG35XqNOtZt7j7X3f8zebEEuK4HOTKaLU2+Zlrd/syskOi3t4zeBt39d+6+xMymkyi/zxKTfZci235ist9amNmFwF4SD9zV5Gjfxb3g234StrSDebNhKTDD3acBvUh8aKttnlQZs5rb3Xe7e+s/mB41Q9azpsj2SxI3ynOAKjObmK9sLcysChgMrOtBjqxdx63y/SfH3v4+l898ZmYkHrx3AN6DHNnO9grx2m+9gW+TeEZGD7N0KV/cCz7fn4Rd6e4tf9ZxWZo8UceyqSe5sp31RXff4+6HSdzxyvOZzcyGAA+QeBofu/3WJl/b219e950n3AasBM7rQY5sZxsRp/1GotjnunvLlxTn7HYX94LP9ydh/83MJplZAfBFEo+ebfOkypjr3FEz5CPrr8xsuJkdD3wGWJ2vbMkjqZ8Bf+3u7/cwR8b3W4p8bW9/r+Yrn5n9DzO7PnlxEHBvD3JkO9sP47LfkmYAt5lZHTAZuKwHWbqWz91j+w8YQOLK+R7wBjAwx+s/i8QRwSrgHhIPiC8A3yfxly9PiTqWpXx1yf9PBl5Lrm8pUNCTsQxnqwHeTO7HP0t3vUYd62GmW0g8ha9L/vtKd3Nk47aZIt/ftb79JefJy22Qj18y+j0wN7kPYrHvUmSbEJf9lup+0ZP91NV9l/HSyfS/5JV3DXBivrMk8xQBVwFlXR3Lcq4Ryf00MBNj+bheo47FJUe+bptxuQ3+d9t3cdlvudx3+iSriEig4v4avIiIdJMKXkQkUCp4EZFAqeBFRAKlghcRCdT/B/BZNA+fcDltAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(2000, 100), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >22</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow0_col0\" class=\"data row0 col0\" >光学算法工程师</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow0_col1\" class=\"data row0 col1\" >28611</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow0_col2\" class=\"data row0 col2\" >24500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow0_col3\" class=\"data row0 col3\" >26214</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow0_col4\" class=\"data row0 col4\" >40000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow0_col5\" class=\"data row0 col5\" >9</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >23</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow1_col0\" class=\"data row1 col0\" >CT重建</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow1_col1\" class=\"data row1 col1\" >27900</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow1_col2\" class=\"data row1 col2\" >19500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow1_col3\" class=\"data row1 col3\" >30000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow1_col4\" class=\"data row1 col4\" >30000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow1_col5\" class=\"data row1 col5\" >5</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow1_col6\" class=\"data row1 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow2_col0\" class=\"data row2 col0\" >系统架构师</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow2_col1\" class=\"data row2 col1\" >26170</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow2_col3\" class=\"data row2 col3\" >25000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow2_col4\" class=\"data row2 col4\" >52500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow2_col5\" class=\"data row2 col5\" >5229</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow2_col6\" class=\"data row2 col6\" >5.39%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >17</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow3_col0\" class=\"data row3 col0\" >规划算法工程师</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow3_col1\" class=\"data row3 col1\" >25968</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow3_col2\" class=\"data row3 col2\" >18500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow3_col3\" class=\"data row3 col3\" >24375</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow3_col4\" class=\"data row3 col4\" >37500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow3_col5\" class=\"data row3 col5\" >31</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow3_col6\" class=\"data row3 col6\" >0.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >21</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow4_col0\" class=\"data row4 col0\" >adas</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow4_col1\" class=\"data row4 col1\" >25575</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow4_col2\" class=\"data row4 col2\" >10416.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow4_col3\" class=\"data row4 col3\" >26667</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow4_col4\" class=\"data row4 col4\" >37500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow4_col5\" class=\"data row4 col5\" >21</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.02%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow5_col0\" class=\"data row5 col0\" >机器学习</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow5_col1\" class=\"data row5 col1\" >24930</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow5_col2\" class=\"data row5 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow5_col3\" class=\"data row5 col3\" >22500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow5_col4\" class=\"data row5 col4\" >54166.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow5_col5\" class=\"data row5 col5\" >2376</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow5_col6\" class=\"data row5 col6\" >2.45%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >8</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow6_col0\" class=\"data row6 col0\" >SLAM</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow6_col1\" class=\"data row6 col1\" >23053</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow6_col2\" class=\"data row6 col2\" >9500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow6_col3\" class=\"data row6 col3\" >22500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow6_col4\" class=\"data row6 col4\" >44925.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow6_col5\" class=\"data row6 col5\" >346</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow6_col6\" class=\"data row6 col6\" >0.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >13</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow7_col0\" class=\"data row7 col0\" >FPGA</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow7_col1\" class=\"data row7 col1\" >22370</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow7_col2\" class=\"data row7 col2\" >10000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow7_col3\" class=\"data row7 col3\" >21547</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow7_col4\" class=\"data row7 col4\" >37500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow7_col5\" class=\"data row7 col5\" >197</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >14</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow8_col0\" class=\"data row8 col0\" >图像算法</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow8_col1\" class=\"data row8 col1\" >22185</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow8_col2\" class=\"data row8 col2\" >8560.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow8_col3\" class=\"data row8 col3\" >21000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow8_col4\" class=\"data row8 col4\" >47551.887500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow8_col5\" class=\"data row8 col5\" >434</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow8_col6\" class=\"data row8 col6\" >0.45%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >2</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow9_col0\" class=\"data row9 col0\" >算法工程师</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow9_col1\" class=\"data row9 col1\" >20416</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow9_col2\" class=\"data row9 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow9_col3\" class=\"data row9 col3\" >17500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow9_col4\" class=\"data row9 col4\" >40000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow9_col5\" class=\"data row9 col5\" >4567</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow9_col6\" class=\"data row9 col6\" >4.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >6</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow10_col0\" class=\"data row10 col0\" >大数据</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow10_col1\" class=\"data row10 col1\" >19499</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow10_col2\" class=\"data row10 col2\" >7132.500000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow10_col3\" class=\"data row10 col3\" >17500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow10_col4\" class=\"data row10 col4\" >40000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow10_col5\" class=\"data row10 col5\" >1393</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow10_col6\" class=\"data row10 col6\" >1.44%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row11\" class=\"row_heading level0 row11\" >4</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow11_col0\" class=\"data row11 col0\" >DSP</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow11_col1\" class=\"data row11 col1\" >19332</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow11_col2\" class=\"data row11 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow11_col3\" class=\"data row11 col3\" >17500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow11_col4\" class=\"data row11 col4\" >45000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow11_col5\" class=\"data row11 col5\" >1954</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow11_col6\" class=\"data row11 col6\" >2.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row12\" class=\"row_heading level0 row12\" >19</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow12_col0\" class=\"data row12 col0\" >cae</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow12_col1\" class=\"data row12 col1\" >19156</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow12_col2\" class=\"data row12 col2\" >3850.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow12_col3\" class=\"data row12 col3\" >20000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow12_col4\" class=\"data row12 col4\" >25000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow12_col5\" class=\"data row12 col5\" >64</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow12_col6\" class=\"data row12 col6\" >0.07%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row13\" class=\"row_heading level0 row13\" >16</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow13_col0\" class=\"data row13 col0\" >信号处理</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow13_col1\" class=\"data row13 col1\" >19077</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow13_col2\" class=\"data row13 col2\" >10925.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow13_col3\" class=\"data row13 col3\" >17500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow13_col4\" class=\"data row13 col4\" >37250.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow13_col5\" class=\"data row13 col5\" >86</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow13_col6\" class=\"data row13 col6\" >0.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row14\" class=\"row_heading level0 row14\" >7</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow14_col0\" class=\"data row14 col0\" >视觉软件工程师</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow14_col1\" class=\"data row14 col1\" >19048</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow14_col2\" class=\"data row14 col2\" >5317.857143</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow14_col3\" class=\"data row14 col3\" >17500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow14_col4\" class=\"data row14 col4\" >41750.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow14_col5\" class=\"data row14 col5\" >1278</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow14_col6\" class=\"data row14 col6\" >1.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row15\" class=\"row_heading level0 row15\" >9</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow15_col0\" class=\"data row15 col0\" >机器人</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow15_col1\" class=\"data row15 col1\" >17636</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow15_col2\" class=\"data row15 col2\" >5750.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow15_col3\" class=\"data row15 col3\" >17500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow15_col4\" class=\"data row15 col4\" >38791.666667</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow15_col5\" class=\"data row15 col5\" >289</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow15_col6\" class=\"data row15 col6\" >0.30%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row16\" class=\"row_heading level0 row16\" >12</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow16_col0\" class=\"data row16 col0\" >Unity3d</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow16_col1\" class=\"data row16 col1\" >17588</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow16_col2\" class=\"data row16 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow16_col3\" class=\"data row16 col3\" >15000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow16_col4\" class=\"data row16 col4\" >34906.250000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow16_col5\" class=\"data row16 col5\" >586</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow16_col6\" class=\"data row16 col6\" >0.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row17\" class=\"row_heading level0 row17\" >1</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow17_col0\" class=\"data row17 col0\" >一般程序员</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow17_col1\" class=\"data row17 col1\" >15854</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow17_col2\" class=\"data row17 col2\" >6000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow17_col3\" class=\"data row17 col3\" >14000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow17_col4\" class=\"data row17 col4\" >37500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow17_col5\" class=\"data row17 col5\" >76346</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow17_col6\" class=\"data row17 col6\" >78.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row18\" class=\"row_heading level0 row18\" >11</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow18_col0\" class=\"data row18 col0\" >遥感</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow18_col1\" class=\"data row18 col1\" >15034</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow18_col2\" class=\"data row18 col2\" >9500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow18_col3\" class=\"data row18 col3\" >13500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow18_col4\" class=\"data row18 col4\" >27075.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow18_col5\" class=\"data row18 col5\" >79</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow18_col6\" class=\"data row18 col6\" >0.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row19\" class=\"row_heading level0 row19\" >10</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow19_col0\" class=\"data row19 col0\" >gis</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow19_col1\" class=\"data row19 col1\" >14817</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow19_col2\" class=\"data row19 col2\" >6225.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow19_col3\" class=\"data row19 col3\" >13000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow19_col4\" class=\"data row19 col4\" >25000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow19_col5\" class=\"data row19 col5\" >487</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow19_col6\" class=\"data row19 col6\" >0.50%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row20\" class=\"row_heading level0 row20\" >18</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow20_col0\" class=\"data row20 col0\" >爬虫工程师</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow20_col1\" class=\"data row20 col1\" >14297</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow20_col2\" class=\"data row20 col2\" >6400.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow20_col3\" class=\"data row20 col3\" >13000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow20_col4\" class=\"data row20 col4\" >24500.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow20_col5\" class=\"data row20 col5\" >148</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow20_col6\" class=\"data row20 col6\" >0.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row21\" class=\"row_heading level0 row21\" >15</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow21_col0\" class=\"data row21 col0\" >etl</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow21_col1\" class=\"data row21 col1\" >14104</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow21_col2\" class=\"data row21 col2\" >6225.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow21_col3\" class=\"data row21 col3\" >13500</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow21_col4\" class=\"data row21 col4\" >20196.350000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow21_col5\" class=\"data row21 col5\" >407</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow21_col6\" class=\"data row21 col6\" >0.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row22\" class=\"row_heading level0 row22\" >0</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow22_col0\" class=\"data row22 col0\" >数据分析</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow22_col1\" class=\"data row22 col1\" >11862</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow22_col2\" class=\"data row22 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow22_col3\" class=\"data row22 col3\" >9000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow22_col4\" class=\"data row22 col4\" >29166.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow22_col5\" class=\"data row22 col5\" >689</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow22_col6\" class=\"data row22 col6\" >0.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031eflevel0_row23\" class=\"row_heading level0 row23\" >20</th>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow23_col0\" class=\"data row23 col0\" >生物信息工程师</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow23_col1\" class=\"data row23 col1\" >11450</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow23_col2\" class=\"data row23 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow23_col3\" class=\"data row23 col3\" >12929</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow23_col4\" class=\"data row23 col4\" >15000.000000</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow23_col5\" class=\"data row23 col5\" >20</td>\n",
       "                        <td id=\"T_be6be1ca_74f5_11ea_aa8b_701ce71031efrow23_col6\" class=\"data row23 col6\" >0.02%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1969b98b288>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17061.50576560423"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97041"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30429.000000\n",
       "mean         3.189096\n",
       "std          4.033830\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          5.000000\n",
       "max        280.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97041"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount_by_cities = data.groupby(by='city').headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "上海    33597\n",
       "北京    17560\n",
       "广州    19771\n",
       "深圳    26113\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount_by_cities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(headcount_by_cities.values, labels=headcount_by_cities.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow0_col0\" class=\"data row0 col0\" >北京</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow0_col1\" class=\"data row0 col1\" >19268</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow0_col2\" class=\"data row0 col2\" >6500</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow0_col4\" class=\"data row0 col4\" >45000</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow0_col5\" class=\"data row0 col5\" >17560</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow0_col6\" class=\"data row0 col6\" >18.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow1_col0\" class=\"data row1 col0\" >上海</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow1_col1\" class=\"data row1 col1\" >17415</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow1_col4\" class=\"data row1 col4\" >40000</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow1_col5\" class=\"data row1 col5\" >33597</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow1_col6\" class=\"data row1 col6\" >34.62%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow2_col0\" class=\"data row2 col0\" >深圳</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow2_col1\" class=\"data row2 col1\" >17169</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow2_col2\" class=\"data row2 col2\" >6500</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow2_col5\" class=\"data row2 col5\" >26113</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow2_col6\" class=\"data row2 col6\" >26.91%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow3_col0\" class=\"data row3 col0\" >广州</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow3_col1\" class=\"data row3 col1\" >14360</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow3_col2\" class=\"data row3 col2\" >5328</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow3_col5\" class=\"data row3 col5\" >19771</td>\n",
       "                        <td id=\"T_c44d4cbe_74f5_11ea_b243_701ce71031efrow3_col6\" class=\"data row3 col6\" >20.37%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196a020de48>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_beijing=data[data.city=='北京'].monthly_salary\n",
    "salary_shanghai=data[data.city=='上海'].monthly_salary\n",
    "salary_guangzhou=data[data.city=='广州'].monthly_salary\n",
    "salary_shenzhen=data[data.city=='深圳'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=151.2824673196333, pvalue=1.3354877293755416e-34)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_beijing, salary_shanghai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=37.25935693476756, pvalue=1.0538712751880982e-09)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shanghai, salary_shenzhen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=503.71444176979026, pvalue=8.357616969649521e-110)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shenzhen, salary_guangzhou)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c84817ca_74f5_11ea_8062_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col1\" class=\"data row0 col1\" >rust</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col2\" class=\"data row0 col2\" >25257</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col4\" class=\"data row0 col4\" >9000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col6\" class=\"data row0 col6\" >242</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col1\" class=\"data row1 col1\" >ruby</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col2\" class=\"data row1 col2\" >23824</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col4\" class=\"data row1 col4\" >6145</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col5\" class=\"data row1 col5\" >45000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col6\" class=\"data row1 col6\" >529</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col1\" class=\"data row2 col1\" >scala</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col2\" class=\"data row2 col2\" >23030</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col3\" class=\"data row2 col3\" >20833</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col4\" class=\"data row2 col4\" >8342</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col5\" class=\"data row2 col5\" >45000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col6\" class=\"data row2 col6\" >1157</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.75%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col1\" class=\"data row3 col1\" >python</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col2\" class=\"data row3 col2\" >21944</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col3\" class=\"data row3 col3\" >20000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col4\" class=\"data row3 col4\" >7000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col5\" class=\"data row3 col5\" >45000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col6\" class=\"data row3 col6\" >14077</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow3_col7\" class=\"data row3 col7\" >9.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col1\" class=\"data row4 col1\" >go</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col2\" class=\"data row4 col2\" >21766</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col3\" class=\"data row4 col3\" >20000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col4\" class=\"data row4 col4\" >7554</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col5\" class=\"data row4 col5\" >45000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col6\" class=\"data row4 col6\" >13335</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow4_col7\" class=\"data row4 col7\" >8.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col1\" class=\"data row5 col1\" >lua</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col2\" class=\"data row5 col2\" >21371</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col3\" class=\"data row5 col3\" >20000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col4\" class=\"data row5 col4\" >7000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col5\" class=\"data row5 col5\" >42071</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col6\" class=\"data row5 col6\" >1312</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.85%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col1\" class=\"data row6 col1\" >r</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col2\" class=\"data row6 col2\" >20517</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col4\" class=\"data row6 col4\" >5833</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col5\" class=\"data row6 col5\" >47175</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col6\" class=\"data row6 col6\" >1293</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col2\" class=\"data row7 col2\" >20213</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col3\" class=\"data row7 col3\" >19000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col4\" class=\"data row7 col4\" >7000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col5\" class=\"data row7 col5\" >40000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col6\" class=\"data row7 col6\" >2708</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow7_col7\" class=\"data row7 col7\" >1.75%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col1\" class=\"data row8 col1\" >perl</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col2\" class=\"data row8 col2\" >18982</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col3\" class=\"data row8 col3\" >17500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col4\" class=\"data row8 col4\" >5250</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col5\" class=\"data row8 col5\" >43833</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col6\" class=\"data row8 col6\" >1022</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.66%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col1\" class=\"data row9 col1\" >typescript</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col2\" class=\"data row9 col2\" >17839</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col3\" class=\"data row9 col3\" >16333</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col4\" class=\"data row9 col4\" >7312</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col5\" class=\"data row9 col5\" >37500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col6\" class=\"data row9 col6\" >390</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col1\" class=\"data row10 col1\" >cpp</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col2\" class=\"data row10 col2\" >17615</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col3\" class=\"data row10 col3\" >15000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col4\" class=\"data row10 col4\" >6000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col5\" class=\"data row10 col5\" >40000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col6\" class=\"data row10 col6\" >39916</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow10_col7\" class=\"data row10 col7\" >25.75%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col1\" class=\"data row11 col1\" >php</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col2\" class=\"data row11 col2\" >17541</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col3\" class=\"data row11 col3\" >15000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col4\" class=\"data row11 col4\" >5250</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col5\" class=\"data row11 col5\" >42500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col6\" class=\"data row11 col6\" >5915</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow11_col7\" class=\"data row11 col7\" >3.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col1\" class=\"data row12 col1\" >swift</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col2\" class=\"data row12 col2\" >17446</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col3\" class=\"data row12 col3\" >16000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col4\" class=\"data row12 col4\" >7525</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col5\" class=\"data row12 col5\" >35049</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col6\" class=\"data row12 col6\" >966</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.62%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col1\" class=\"data row13 col1\" >kotlin</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col2\" class=\"data row13 col2\" >17401</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col3\" class=\"data row13 col3\" >17500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col4\" class=\"data row13 col4\" >8000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col5\" class=\"data row13 col5\" >34208</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col6\" class=\"data row13 col6\" >378</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col1\" class=\"data row14 col1\" >java</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col2\" class=\"data row14 col2\" >16793</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col3\" class=\"data row14 col3\" >15000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col4\" class=\"data row14 col4\" >7000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col5\" class=\"data row14 col5\" >37500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col6\" class=\"data row14 col6\" >42023</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow14_col7\" class=\"data row14 col7\" >27.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col1\" class=\"data row15 col1\" >haskell</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col2\" class=\"data row15 col2\" >15000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col3\" class=\"data row15 col3\" >15000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col4\" class=\"data row15 col4\" >11500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col5\" class=\"data row15 col5\" >22000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col6\" class=\"data row15 col6\" >28</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow15_col7\" class=\"data row15 col7\" >0.02%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col1\" class=\"data row16 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col2\" class=\"data row16 col2\" >14281</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col3\" class=\"data row16 col3\" >12500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col4\" class=\"data row16 col4\" >5250</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col5\" class=\"data row16 col5\" >37500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col6\" class=\"data row16 col6\" >15032</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow16_col7\" class=\"data row16 col7\" >9.70%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col1\" class=\"data row17 col1\" >objective_c</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col2\" class=\"data row17 col2\" >14144</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col3\" class=\"data row17 col3\" >13000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col4\" class=\"data row17 col4\" >6250</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col5\" class=\"data row17 col5\" >28125</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col6\" class=\"data row17 col6\" >85</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow17_col7\" class=\"data row17 col7\" >0.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col1\" class=\"data row18 col1\" >javascript</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col2\" class=\"data row18 col2\" >13904</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col3\" class=\"data row18 col3\" >12500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col4\" class=\"data row18 col4\" >5250</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col5\" class=\"data row18 col5\" >27500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col6\" class=\"data row18 col6\" >14050</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow18_col7\" class=\"data row18 col7\" >9.06%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col2\" class=\"data row19 col2\" >13007</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col3\" class=\"data row19 col3\" >12500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col4\" class=\"data row19 col4\" >5517</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col5\" class=\"data row19 col5\" >22942</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col6\" class=\"data row19 col6\" >367</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col1\" class=\"data row20 col1\" >julia</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col2\" class=\"data row20 col2\" >12500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col3\" class=\"data row20 col3\" >12500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col4\" class=\"data row20 col4\" >12500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col5\" class=\"data row20 col5\" >12500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col6\" class=\"data row20 col6\" >5</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col0\" class=\"data row21 col0\" >22</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col1\" class=\"data row21 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col2\" class=\"data row21 col2\" >12399</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col3\" class=\"data row21 col3\" >12167</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col4\" class=\"data row21 col4\" >6250</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col5\" class=\"data row21 col5\" >22500</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col6\" class=\"data row21 col6\" >19</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow21_col7\" class=\"data row21 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col0\" class=\"data row22 col0\" >23</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col1\" class=\"data row22 col1\" >vba</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col2\" class=\"data row22 col2\" >12319</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col3\" class=\"data row22 col3\" >12000</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col4\" class=\"data row22 col4\" >6212</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col5\" class=\"data row22 col5\" >23538</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col6\" class=\"data row22 col6\" >177</td>\n",
       "                        <td id=\"T_c84817ca_74f5_11ea_8062_701ce71031efrow22_col7\" class=\"data row22 col7\" >0.11%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196a024c908>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "初中及以下        4\n",
       "博士         393\n",
       "大专       23132\n",
       "本科       59567\n",
       "硕士        4089\n",
       "高中         147\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_cb52e258_74f5_11ea_95b1_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow0_col1\" class=\"data row0 col1\" >29286</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow0_col2\" class=\"data row0 col2\" >13711</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow0_col3\" class=\"data row0 col3\" >29697</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow0_col5\" class=\"data row0 col5\" >393</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.45%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow1_col1\" class=\"data row1 col1\" >21928</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow1_col2\" class=\"data row1 col2\" >9000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow1_col5\" class=\"data row1 col5\" >4089</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow1_col6\" class=\"data row1 col6\" >4.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow2_col1\" class=\"data row2 col1\" >17869</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow2_col3\" class=\"data row2 col3\" >15500</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow2_col4\" class=\"data row2 col4\" >40000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow2_col5\" class=\"data row2 col5\" >59567</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow2_col6\" class=\"data row2 col6\" >68.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow3_col1\" class=\"data row3 col1\" >14327</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow3_col2\" class=\"data row3 col2\" >5250</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow3_col4\" class=\"data row3 col4\" >32500</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow3_col5\" class=\"data row3 col5\" >23132</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow3_col6\" class=\"data row3 col6\" >26.49%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow4_col0\" class=\"data row4 col0\" >初中及以下</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow4_col1\" class=\"data row4 col1\" >13750</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow4_col3\" class=\"data row4 col3\" >13750</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow4_col4\" class=\"data row4 col4\" >17500</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow4_col5\" class=\"data row4 col5\" >4</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow5_col0\" class=\"data row5 col0\" >高中</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow5_col1\" class=\"data row5 col1\" >7859</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow5_col2\" class=\"data row5 col2\" >4000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow5_col3\" class=\"data row5 col3\" >7000</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow5_col4\" class=\"data row5 col4\" >16521</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow5_col5\" class=\"data row5 col5\" >147</td>\n",
       "                        <td id=\"T_cb52e258_74f5_11ea_95b1_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.17%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196a30ef748>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是13797，17863，22482，29386。中位数分别是12500， 16000，20833， 27500。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=49.15906338171529, pvalue=3.5953490470403533e-12)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=382.9235065968665, pvalue=1.7255778515048517e-84)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1453.4162488548782, pvalue=1.52740520172859e-309)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_d0a25476_74f5_11ea_a65e_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow0_col1\" class=\"data row0 col1\" >32740</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow0_col2\" class=\"data row0 col2\" >12500</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow0_col4\" class=\"data row0 col4\" >59767</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow0_col5\" class=\"data row0 col5\" >408</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow1_col1\" class=\"data row1 col1\" >22984</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow1_col2\" class=\"data row1 col2\" >11500</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow1_col3\" class=\"data row1 col3\" >20833</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow1_col5\" class=\"data row1 col5\" >14322</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow1_col6\" class=\"data row1 col6\" >14.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow2_col1\" class=\"data row2 col1\" >18254</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow2_col2\" class=\"data row2 col2\" >9000</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow2_col3\" class=\"data row2 col3\" >16000</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow2_col5\" class=\"data row2 col5\" >30961</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow2_col6\" class=\"data row2 col6\" >31.91%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow3_col0\" class=\"data row3 col0\" ></td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow3_col1\" class=\"data row3 col1\" >15265</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow3_col2\" class=\"data row3 col2\" >5000</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow3_col4\" class=\"data row3 col4\" >35410</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow3_col5\" class=\"data row3 col5\" >23504</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow3_col6\" class=\"data row3 col6\" >24.22%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow4_col1\" class=\"data row4 col1\" >13977</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow4_col2\" class=\"data row4 col2\" >6000</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow4_col4\" class=\"data row4 col4\" >35000</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow4_col5\" class=\"data row4 col5\" >27846</td>\n",
       "                        <td id=\"T_d0a25476_74f5_11ea_a65e_701ce71031efrow4_col6\" class=\"data row4 col6\" >28.70%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196a34b5e48>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Od7j33nvDbcyaNYsbb7wxqt3I5blr1y4uv/xyBgwYENWLufXWW+nfvz8/+clPEi7j/fv3c9lllxEIBBg+fDgHDx6MOx+h+f/3f/93Lr300vDf57nnngv/XZ555hn27dvH1VdfTSAQ4Oabbwbgzjvv5KmnngrPe+izSCJJE4KZPej9Xm5my7yf5Wa2LDfhZVZ9fT21tbWMGDGCiooKJk+ezOTJk/nv//5vIPjPFTsOYMCAAaxcuZLOnTvz/PPPs2vXLtatW8fKlSuZOnVqVEJ49tlno9o8ePAgb7/9dtThk9hxH330ER9++CGrVq3in//8Jw8++GDc+H/84x8zY8YMxowZw4oVKzjppJMAeP3111m0aBGTJ09m/vz5Cee/a9euLF++nEAgwKxZs+KWWbduHS+//DJ//vOfmTFjBnV1dfz1r39lxYoVvPLKK0ydOpVbb70VgJtvvpkjjzyS6dOns3nz5oTz68cZZ5zBokWLuOCCC1i8eDG9e/dOWHbr1q2MGjWKuXPncuyxxyYsM27cOJYuXUpDQwMffPABEL0OLFu2jA0bNtC1a1defvll6uvrue222wD45JNPOHDgAJ07d04Yx7333svIkSN56aWX+OSTT/jTn/4EQFVVFa+88grr169P2Ntcv349RUVF1NbWcsMNN7Bnz56E7axatYp+/fqxaNEiAK655hoWLlwIQG1tLUOHDmXmzJn07NmT2tpatm7dyrp16xg9ejRPPvkkAIsWLeKKK65I2IYINJMQnHMTvd8DnXODvJ+BzrlWecnp6NGjATjttNM4cOCA7+nOO+88AHr16kVDQwMlJSWMGTOGb3/72zz22GMJN0oATzzxRNTec7xxxxxzDJ999hl9+vRpUtaPf/u3f6O4uDjufO3fvz/8OXQIp3fv3vz973+PW9eGDRsoKysLlx89ejRvv/02559/PmZG3759efvttwEoLS3liCOOoLS0lKKiooTzm0wovt69e/P0009TVVXFM888E17m8Tz00EN07dqVd999N2F9xcXFPProo4waNYpdu3aFx8euA6eccgpr1qwhEAjw4x//OFzPb37zG773ve8ljX39+vXhZdqnT5/wcvGznHv37k3Pnj0ZMmQIixYt4phjjok7HwA9e/aM6nmeeeaZbNmyhU8//ZTjjz+eDh068M477zBv3jzKy8v5xz/+wfvvv8/XvvY1du/ezYoVK+jZsyft27dPOj8ibeqQUYcOHaKG27dvz759+4DgYZtE4+rq6gB444036NatG5s3b6akpIRFixZxyimnNOkVhHz22Wds3ryZ7t27Jx1XV1fH8OHDee2115g0aVLSeYgXX+x8HXnkkeHDSaE9SYA1a9YAwV5AaWlp3Pp79OgRLvfKK69w/fXXc9ZZZ/H666/jnGPVqlV885vf9D2/8UTGF9qrPvfcc1m2bBmDBw9m0aJFSXsIU6dO5eGHH2bq1KkJ65s1axZXX301c+fOjVo+scvqT3/6E1OnTuXVV18NJ7Lt27dTXFzMCSeckHQ+vvnNb7Jq1SqAqOXiZzm/+eab9O/fn8WLF/PRRx/x0ksvxZ0PgI4dm97fccEFFzBjxgyGDRsGwNe//nUmTJjAihUruOeee8LnPUaOHMmNN94YToQiybSphBCroqKCZ599lv79+/PSSy8lHPf6669TXl7Oxx9/zGWXXUaXLl144YUX6N+/f3gjFhK5J/fYY481OQEZb1yPHj148MEHGThwIFdddVW43XjOPfdc3nnnHQYMGMAf/vCHuGUGDRrECy+8wM0338znn3/xPMJNmzZRXl7OkiVL+P73v98kXgj2gvr160f//v25/fbbufPOO+nZsyfl5eX079+fu+++m/vuuy9uu/HmLWT//v0cddRRAAwbNoxf/vKX3HTTTZSUlADBPebTTz+dM844gy9/+cucfvrpCZfB0UcfzamnnkqPHj2YP39+3PoqKiq47777GDQo2Jl9//3349Z17rnnMm7cOAYNGsTIkSN56623ePTRRxP2DiLn42c/+xlPPfUUF110EccffzxDhgwB4L/+67/o378/X/3qVxP2dEpLS6mpqeHCCy9k27ZtlJWVxZ2PRK655hpmzJjBZZddBsAPfvADFi5cSCAQ4Ne//jWnnnoqAFdffTVmxkUXXZS0PhEguJfZ3A+w1k+5XP6cd955LhfuvPNOt3z58rSm/eCDD3yNe/HFF10gEHCDBw92l19+uZs7d25a7eVbvHn77LPPXJ8+fVy/fv3cypUr8xBVcjNnznQDBw50Q4YMcVVVVW758uVx5+Of//yn69u3r+vfv79bv359HiJN3VtvveXOP/989+ijj+Y7FCkgwGqXYLtqzscVpGY2ATjknKvJcn7yrayszK1evTrfYeRcTU0N9fX1KU+3xbubuGsL7ybu1q2b3vWbotirpI477jief/75/AQjbZ6ZrXHOlcX7zu+dylcQvEv5O8B+wLlWemK5tauvr+eNv70Bx6c4oXch1Hbbnn7jH6c/aVtW6PeNiIT4fafywOZLSc4cD4fKD6U0SdGK4OmiVKeLV4eIHJ58JQQzM6AK6AysB951zv0rm4GJiEhu+d3l+wMwkOAzjIoIPv5aREQOI34TwknOuUnAHufcKylMJyIirYTfDXvo8ddf0eOvRUQOT35PKo81syuADd7PXVmNSkREcs5XD8F7Q1oR0AgcFo++FhGRaH4PGT1F8B3Ke4GhwO+zFpGIiOSF3xvTvuycuzY0YGbLsxSPiIjkid+EsM/MJgNrgAuAT8ws4JyrzV5o2VVTE3wKhx7DcPjT31rEH78J4TXgKOBCb/gNoBxotQkhnecBSeukv7WIP34TwqPOufDzg83sHOdc5t9ELyIieeP3pPIzZva/zexLZvafwL3NTiEiIq2K34TQH/gqsJ3g3cpDmykvIiKtTCrPMjqa4DmEs83soeyFJCIi+eD3HMIvCD5R/xTgdiDxC29FRKRV8ttDGAn8B3AfwUNH1yYvXrh27NjBTTfdxMaNG2lsbMx3OJIngUAg/PPII48QCASYNWsWdXV1lJeXs2bNmiblysvLWbZsGePGjWPnzp3s2LEj/Dm2bKR58+YRCASYO3cu48aNa1Ju48aNVFZWsnr1akaNGkUgEGD58uXh6ebPnx+uK7bNeO3W1dWFh+++++64sUTWGc+NN95IIBBg7NixScstXbo0HG+s2Fj9tp2sjnTmJVmMrVFofcnG1XN+E8K3nHNXAR875/4IHJdKI2Z2oplVmFmnlCPMsDlz5rB+/Xr27dvHtm3b8h2OFIDf/z544/2cOXOYNm0ahw4dYurUqU3KHTp0iHvuuYd169YxZ84c5syZE/6czIwZMwB4+OGHWbduXZPv77nnHvbu3csdd9zB5s2bAbj77rvD002fPj1c1k+b06ZNC39esmRJ3Fgi64wntLHZsGFD0nL33ntvON5YsbH6bTtZHZH81pcsxtYotL7cdVfmHynnNyFsN7M7gBPMrBrwvSU1sxOAFwne0LbczE6KU6admb1nZiu8n2/5rT8VO3bsYMGCBeHhXbt2xd3zkMNb7B58pD179oR/xyt38OBBnHMsWLCAhQsX4pxj4cKFTcqGhufNm0fke8tj32EeCARoaGiIajuyndA08+fPZ8eOHVFt7ty5M267kfXAFxvCyFhCdcZz4403Rg0n6iUsXbqUgwcPhuON3AOPjfWJJ57w1XakePMb4ndeksXYGm3cuDG8vjQ0NGS8l2CxK2jcQmbtgbHA1wk+7fRR59w+Xw2YXQx85pxb5V2yusQ5tyimTG/gOufcrX4DLysrc6tXr/ZbHAjuScyfPz/qn7KkpIRTTz01pXryadOmTew5tIdDl+XhFZovFtGxqCPdu3dPu4582LRpE+3bt+fZZ58FkicEv4IvEQxujIqLi+MefqytreXiiy9ukgTSbW/YsGEsWLCAxsZGiouLqaqq4rnnnvM1fbxYzIyVK1c2KRtv+dTWNr0HddCgQeGNLUC7du1YtmwZEPxfi4w1dvkkajtSbB1VVVVMnDgRwPe8JIuxNRo9enQ4IQCUlpby+OOPp1SHma1xzpXF+85XD8E5t9859wvn3P9yztX4TQbetCu9ZBAg2Et4NU6xvsBlZlZnZrPMLO7JbjMba2arzWz19u2pvyx+yZIlTf45P/roo5TrEXHOhdelZOeiMpEMQvUsWbIk3FZjYyOLFy9OuY5Mxha5oY0djo21uVjiSTa/fuclWYytUWQyiDfcUn6vMmoR753M1wEfEXyEdqzXgcHOua1m9jjBJ6o26QM652YCMyHYQ0g1joqKiiY9hGHDhoX3OlqD8ePH88b7b+Sn8Y7Q/ZTu4WcDtRbZeIaRnx5CqFymeggVFRVRe8xDhgzx3UOIF0toHtLVrl27JnvfIbGxxushNCfe/EZO72deksXYGpWWljbpIWRSTl6F6YJuBtYBw+IUWeec2+p9Xg1k5ZhEdXV11AphZlRXV2ejKTnMFRcXU1xcDEBRUeJ/owkTJmSkvUmTJlFdXR3e8BUVFfledysqKuLGMmnSpLjlu3XrFjXco0ePuOVuu+22qOHIE/Gxscaeh0jUdqRk8+t3XpLF2BpNmTIlaviOO+7IaP1ZTwhmdquZjfYGjwc+jlPsd2Z2tpkdAVwJvJmNWDp16sTQoV/cZH3iiSdSUlKSjaakgMU7Hh7SsWPH8O945dq1a4eZMXToUCorKzEzKisrm5QNDQ8fPjxq7zV2T7a2tja8lxdqO7Kd0DTDhg2jU6dOUW2WlJTEbTeyHvhiIxgZS6jOeH77299GDc+cOTNuucGDB4d3sNq1a8fAgQPD38XG+t3vftdX25HizW+I33lJFmNrdOaZZ4bXl9LS0ibJu6Vy0UOYCVxvZrXAEUC8A593Ab8D1gKvOueWZiuY6upqzjrrLI455hi6dOmSrWakFRk1ahQQXDemTZtGUVFR3EsUi4qKmDJlCr169aK6uprq6urw52RCe7M/+tGP6NWrV5Pvp0yZQocOHbjrrrvCFzhMnTo1PF3k3q+fNiMvOw31DmJjaW4PPbShSdQ7CAntgcfb846N1W/byeqI5Le+ZDG2RqH1JdO9A/B5lVEhSucqo0ih48qt8Xj4G++/kfLVQhm5ymhFEeeecm6rXGbQ+v7WItmQ7CqjnJ9hMbMVMaM+cc5dkes4REQkWs4TgnOuPNdtiohI83JylZGIiBQ+JQQREQGUEERExNO6b9trgUxfvyuFS39rEX/abELIxuMMpDDpby3ijw4ZiYgIoIQgIiIeJQQREQGUEERExKOEICIigBKCiIh4lBBERARQQhAREU+bvTGtVfv4i/cbpDINpDFdbB2npD+5iBQ2JYRWJt3HMGxxWwDoekrX9Bs/RY+BEDmcKSG0MnoMg4hki84hiIgIoIQgIiIeJQQREQGUEERExKOEICIigBKCiIh4lBBERARQQhAREY9uTMujmpoa6uvrU5pmyxbvjuOu/u847tatm25oE5FmKSHkUX19PRvWrqVLCtPs9n5/vGOHr/LbUo5KRNoqJYQ86wJ8D/NdfhYOUpgmVF5EpDk6hyAiIoASgoiIeJQQREQEUEIQERGPEoKIiABKCCIi4lFCEBERQAlBREQ8bToh1NTUUFNTk+8w2gwtb5HC1qbvVE71OULSMlreIoWtTfcQRETkC0oIIiICKCGIiIhHCUFERAAlBBER8SghiIgIUGAJwcxONrPBZnZsvmOR7AsEAuEfgB07djBu3DjGjRtHIBBg7NixUWViy+3cuZPrrruOQCDAddddF54utuzIkSMJBAI8+eST4emqqqoIBAIMHjyYQCDAJZdcEvey2Nj6UpmfRG655RYCgQATJkxIWm7evHkEAgHmz5+fUn11dXWUl5ezZs2aZmNuadupyERckSLXg1y2mw1+5yXbcpYQzKyzmb2U5PszgT8A/YGVZnZkrmKTwjBnzhzWrVvHm2++CcCGDRuSlpszZw5bt24FYOvWraxbty5u2X/9618A/PrXvw5Pt3t38GWkBw4cAKCxsZG77ror4/MUTyjOv/zlL0nLzZgxA4Dp06enVN+0adM4dOgQU6dOTTtGv22nIhNxRYpcD3LZbjb4nZdsy0lCMHcMlK8AAApfSURBVLMTgDlAhyTFegE3OOf+A/gH8NVcxCb5EbsXHQgEWLhwIc4lf+VnZLnnnnsu6rvYaePt4cabLqShoSGqlxAvxmRx+Sl7yy23RA0n6iXMmzcvPD/OuYR76rH1jRkzhj179gCwZ8+etPaK/badirq6uhbHFWnHjh3h9WDhwoUJ96wz3W42+J2XXLDm/gEz0ojZlwADnnfOlScp1w64FJgIDHHOfZ6obFlZmVu9enWL4hoxYgT79++ne/fuLaonXZs2baJo715+msV3Kj+A41CHDnmbx0ibNm2iffv2PPvss3E3mMXFxTQ2NjZbj99y6SgtLeXxxx8H4m/Ua2tr407nt6zfchdffHFUgjMzVq5c6au+SB07dmTBggVJy6TbdiqGDh0a3jCnG1ek6dOns2DBAhobGykuLqaqqoqJEydmvd1s8DsvmWJma5xzZfG+y0kPwTn3qXPuEx9FOwLXAu9C07fDm9lYM1ttZqu3b9+e6TAlz/xu5LOVDCDYSygEsTtq6e64RW4Mc912sjjSiSvSkiVLwutBY2Mjixcvzkm72eB3XnKhoJ5l5Jz7GKg2s98B5wOvxXw/E5gJwR5CS9vr2rUrQN4euDZ+/Hg+Xrs2q22UAMd3714QD5UbP3580u8LpYdQCMysyV56Ojp27Ji3tmPjiN1Tb4mKioqoveohQ4bkpN1s8DsvuVAwVxmZ2cNmFur/Hg98nM94JPf8bnhS2UClujG74447Uiqfql69ekUN9+7dO2652HMLkyZN8lXfGWecETV89913pxqi77ZTMW3atKjhdOKKVF1dHf7bFhUVUV1dnZN2s8HvvORCwSQE4AHgXu9KpDrn3Dv5DkiyJ/a4eW1tLZWVlc1uwCPLXXnllVHfxU5bW1vLsGHDmpSJnS6ktLSUbt26JY0xWVx+yj700ENRw6GreWINHz48PD9m1mQ+EtU3e/bs8F5wx44dOe+88xLGnIjftlNxwQUXtDiuSJ06dQqvB5WVlZSUlOSk3WzwOy+5kNOEkOyEsnPun865i5xzA5xzhZfGJeuqq6vp1asXZ599NgA9evRIWq66upqTTz4ZgJNPPrnJ3nKo7Fe+8hUAbrrppvB0xx4bvNXlyCODVzcXFxdnvXcQEoozUe8gJLSn3tweemx906ZNo6ioqEV7w37bTkUm4ooUuR7kst1s8Dsv2ZaTq4yiGjTrAjwVM/od59wPU6knE1cZhY5p5/scgt8rhiD1q4xm4Tj+nHMK6hxCIcQi0lYlu8oo5yeVnXPbgPJctysiIskV0jkEERHJIyUEEREBlBBERMSjhCAiIkCB3amca5HXnEv2aXmLFLY2nRCae5SCZJaWt0hh0yEjEREBlBBERMSjhCAiIoASgoiIeJQQREQEUEIQERGPEoKIiABt/D6EQrCNLx5p7cdW77ffabYRfP2ciEhzlBDyKJ07d/ds2QLA8d77oJtzfJrtiEjbo4SQR7pzV0QKic4hiIgIoIQgIiIeJQQREQGUEERExGPO+b/ksZCY2XbgXZ/FOwE7shhOJrSGGKF1xKkYM0MxZkahxXi6c+6keF+02oSQCjNb7Zwry3ccybSGGKF1xKkYM0MxZkZriDFEh4xERARQQhAREU9bSQgz8x2AD60hRmgdcSrGzFCMmdEaYgTayDkEERFpXlvpIYiISDOUEKRVMLMTzazCzDrlOxaRw9VhnxDMbJaZvWpmU/LUfmcze8n7XGxmL5jZK2Z2Y0vHZSC248xsoZktNrN5ZnZkvOXVknEZivME4EXgAmC5mZ1UoHF2NrM3WhpLFuNrZ2bvmdkK7+dbZvYfZva6mf3fiHJpj8tgrL8ys8u9zwW1LM3sRxHLcK2ZPVJoMabrsE4IZjYCOMI51w84w8y657j9E4A5QAdv1DhgjXOuP3C1mR3bwnEtNQp40Dk3hOCrE0YSs7ziLUO/4zIQX0gvYKJz7ufAImBQgcb5n0D7lsSSg+U41zlX7pwrB44ELiKYaD80s8Fmdl664zIVpJkNALo4514oxGXpnHs4Yhm+BPy90GJM12GdEIBy4Gnv82KCK3AufQ5cB3waJ55aoKyF41rEOfcr59wSb/Ak4Ls0XV7lLRiXEc65lc65VWYWILgBurTQ4jSzQcBegom1JbFkJT5PX+AyM6szs1nAJcD/c8ErSxYBA4CLWzCuxcysGPgN0GBmV1C4yxIzOwXoDHQt1BhTdbgnhA7A+97nXQT/eDnjnPvUOfdJM/G0ZFxGmFk/4ARgcyHG58VoBJPrR4ArpDjN7EhgKjDZG1WQf2fgdWCwc+4CoBhoX4BxjgbWAw8QTP43F2CMITcDD7cwnrxuo2Id7glhD8GVHqAj+Z/fePG0ZFyLmdmJwC+BGwsxvhAXdDOwDriwwOKcDPzKOfexN1yoy3Gdcy70FtbVBRrnucBM59w24AmCveFCixEzKwIGAitaGE9BbaPyvYHMtjV80QU7G2jIXyhA/HhaMq5FvD3bZ4CfOefeLbT4IuK81cxGe4PHA/cXWJyDgZvNbAVwDnB5gcUX8jszO9vMjgCuJLh3Wmhx1gNneJ/LgNICjBGCh8he8w6ZFeT/TVqcc4ftD/Al4E3gQeBt4Lg8xbHC+3068DfgFwS770e0ZFwG4voRwUMwK7yf6tjlFW8Z+h2XweV3ArCE4N7ir7z2Ci7O0N+6JbFkeTn2JNjD+ivwc4I7hK9469Q7wFdbMi5DMR5LcCelFnjVW+8LcVneC4zwPhfk3zut+cpn4zmZweDG5FqCVy0UQjxf8eI5LhPjcrG8WjKurcZZ6PFFtNUeuBo4IxPj2uqybA0x+vnRoytERAQ4/M8hiIiIT0oIIiICKCGIiIhHCUFERAAlBBEAzKzczEojhmdHDseUXZGDeLLehkgsJQSRoHKCN0GJtFlKCHLYMLM1Fnyc9/Nm9pqZ3WbBR4a/ZGYzvDKzzewOb9yfzay9mT0GjAFmmNnvI6ocHVkuQZtzzKxvRN19E5T7spktN7OXzewRb9xXvOGXzOznSearSTkzKzWz35vZY178vmMRSUQJQQ4nxwDXEHzM83eA7sBTzrkBwHFm9m2vXEdv3AbgXOfcDcBsYIJzblREfVHlErT5ODDKewzIN5xzqxKUGwD81Tl3EVDrPQvnFILPQaok+LiLRBKVuxx4xIs/lVhE4lJCkMPJB865PcC7BB893hl4zfvuNeAb3uc53u/3CL4TIBE/5ZYD/YAqYH6SuhYCR5jZEqCHc+4QcJDghv5Rgo9sSCRRucUxG32/sYjEpYQgh7O9BN8BgPf7bxHjY+0n2MMIPWo7Ubko3oZ9CcGX4zyRpGg/4HfOuQpgkJl9DZgI3Ad8n+AjvRNJVG5PmrGIxKWEIIezo4CRZvYy8LFzbnGSsv8PmGxmq4CvpdjOM8B7LvjE2ET+ATxgZq8CHxLsxbwI/Jrg3vw+74Ur8fgt5zcWkbj0LCORFjCzSwi+zOV259yfFIu0ZkoIIhlkZl2Ap2JGv+Oc+2E+4hFJhRKCiIgAOocgIiIeJQQREQGUEERExKOEICIiAPx/F3cWt3yveOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_d35a9efe_74f5_11ea_8858_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_type</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow0_col0\" class=\"data row0 col0\" >外资（欧美）</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow0_col1\" class=\"data row0 col1\" >18967</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow0_col4\" class=\"data row0 col4\" >40000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow0_col5\" class=\"data row0 col5\" >5638</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow0_col6\" class=\"data row0 col6\" >5.81%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow1_col0\" class=\"data row1 col0\" >合资</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow1_col1\" class=\"data row1 col1\" >17596</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow1_col3\" class=\"data row1 col3\" >15500</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow1_col4\" class=\"data row1 col4\" >37500</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow1_col5\" class=\"data row1 col5\" >7595</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow1_col6\" class=\"data row1 col6\" >7.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow2_col0\" class=\"data row2 col0\" >国企</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow2_col1\" class=\"data row2 col1\" >17186</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow2_col4\" class=\"data row2 col4\" >45000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow2_col5\" class=\"data row2 col5\" >7915</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow2_col6\" class=\"data row2 col6\" >8.16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow3_col0\" class=\"data row3 col0\" >民营公司</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow3_col1\" class=\"data row3 col1\" >16883</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow3_col2\" class=\"data row3 col2\" >6000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow3_col4\" class=\"data row3 col4\" >39000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow3_col5\" class=\"data row3 col5\" >69561</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow3_col6\" class=\"data row3 col6\" >71.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow4_col0\" class=\"data row4 col0\" >外资（非欧美）</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow4_col1\" class=\"data row4 col1\" >16652</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow4_col2\" class=\"data row4 col2\" >6941</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow4_col3\" class=\"data row4 col3\" >14500</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow4_col4\" class=\"data row4 col4\" >37500</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow4_col5\" class=\"data row4 col5\" >5527</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow4_col6\" class=\"data row4 col6\" >5.70%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow5_col0\" class=\"data row5 col0\" >事业单位</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow5_col1\" class=\"data row5 col1\" >16121</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow5_col2\" class=\"data row5 col2\" >7000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow5_col3\" class=\"data row5 col3\" >14583</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow5_col4\" class=\"data row5 col4\" >35236</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow5_col5\" class=\"data row5 col5\" >683</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.70%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow6_col0\" class=\"data row6 col0\" >非营利组织</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow6_col1\" class=\"data row6 col1\" >14579</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow6_col2\" class=\"data row6 col2\" >7333</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow6_col3\" class=\"data row6 col3\" >14000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow6_col4\" class=\"data row6 col4\" >31874</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow6_col5\" class=\"data row6 col5\" >90</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow6_col6\" class=\"data row6 col6\" >0.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow7_col0\" class=\"data row7 col0\" >外企代表处</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow7_col1\" class=\"data row7 col1\" >10469</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow7_col2\" class=\"data row7 col2\" >1750</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow7_col3\" class=\"data row7 col3\" >8000</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow7_col4\" class=\"data row7 col4\" >22950</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow7_col5\" class=\"data row7 col5\" >32</td>\n",
       "                        <td id=\"T_d35a9efe_74f5_11ea_8858_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.03%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196a3176388>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_size</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000+</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow0_col1\" class=\"data row0 col1\" >26316</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow0_col4\" class=\"data row0 col4\" >46338</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow0_col5\" class=\"data row0 col5\" >6553</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow0_col6\" class=\"data row0 col6\" >6.75%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow1_col0\" class=\"data row1 col0\" >5000-10000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow1_col1\" class=\"data row1 col1\" >19612</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow1_col3\" class=\"data row1 col3\" >17100</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow1_col4\" class=\"data row1 col4\" >45192</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow1_col5\" class=\"data row1 col5\" >2565</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow1_col6\" class=\"data row1 col6\" >2.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow2_col0\" class=\"data row2 col0\" >1000-5000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow2_col1\" class=\"data row2 col1\" >17736</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow2_col3\" class=\"data row2 col3\" >16500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow2_col5\" class=\"data row2 col5\" >11669</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow2_col6\" class=\"data row2 col6\" >12.02%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow3_col0\" class=\"data row3 col0\" >500-1000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow3_col1\" class=\"data row3 col1\" >17257</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow3_col2\" class=\"data row3 col2\" >7000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow3_col5\" class=\"data row3 col5\" >9929</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow3_col6\" class=\"data row3 col6\" >10.23%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow4_col0\" class=\"data row4 col0\" >150-500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow4_col1\" class=\"data row4 col1\" >16465</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow4_col2\" class=\"data row4 col2\" >6500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow4_col4\" class=\"data row4 col4\" >36652</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow4_col5\" class=\"data row4 col5\" >22945</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow4_col6\" class=\"data row4 col6\" >23.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow5_col0\" class=\"data row5 col0\" >50-150</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow5_col1\" class=\"data row5 col1\" >15918</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow5_col4\" class=\"data row5 col4\" >35000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow5_col5\" class=\"data row5 col5\" >28448</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow5_col6\" class=\"data row5 col6\" >29.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow6_col0\" class=\"data row6 col0\" >50-</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow6_col1\" class=\"data row6 col1\" >15038</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow6_col2\" class=\"data row6 col2\" >5500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow6_col3\" class=\"data row6 col3\" >12500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow6_col4\" class=\"data row6 col4\" >42500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow6_col5\" class=\"data row6 col5\" >13197</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow6_col6\" class=\"data row6 col6\" >13.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow7_col0\" class=\"data row7 col0\" ></td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow7_col1\" class=\"data row7 col1\" >14713</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow7_col2\" class=\"data row7 col2\" >7000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow7_col3\" class=\"data row7 col3\" >12500</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow7_col4\" class=\"data row7 col4\" >30000</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow7_col5\" class=\"data row7 col5\" >1735</td>\n",
       "                        <td id=\"T_d4087be6_74f5_11ea_a3eb_701ce71031efrow7_col6\" class=\"data row7 col6\" >1.79%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196a3210848>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >industry</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow0_col0\" class=\"data row0 col0\" >finance</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow0_col1\" class=\"data row0 col1\" >18202</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow0_col4\" class=\"data row0 col4\" >37500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow0_col5\" class=\"data row0 col5\" >3517</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow0_col6\" class=\"data row0 col6\" >3.62%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow1_col0\" class=\"data row1 col0\" ></td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow1_col1\" class=\"data row1 col1\" >18007</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow1_col2\" class=\"data row1 col2\" >6938</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow1_col4\" class=\"data row1 col4\" >35000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow1_col5\" class=\"data row1 col5\" >430</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow1_col6\" class=\"data row1 col6\" >0.44%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow2_col0\" class=\"data row2 col0\" >computer</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow2_col1\" class=\"data row2 col1\" >17256</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow2_col2\" class=\"data row2 col2\" >6000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow2_col4\" class=\"data row2 col4\" >40000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow2_col5\" class=\"data row2 col5\" >74483</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow2_col6\" class=\"data row2 col6\" >76.75%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow3_col0\" class=\"data row3 col0\" >edu</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow3_col1\" class=\"data row3 col1\" >16924</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow3_col2\" class=\"data row3 col2\" >7000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow3_col4\" class=\"data row3 col4\" >40000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow3_col5\" class=\"data row3 col5\" >5632</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow3_col6\" class=\"data row3 col6\" >5.80%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow4_col0\" class=\"data row4 col0\" >service</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow4_col1\" class=\"data row4 col1\" >16217</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow4_col5\" class=\"data row4 col5\" >544</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.56%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow5_col0\" class=\"data row5 col0\" >medical</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow5_col1\" class=\"data row5 col1\" >16138</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow5_col2\" class=\"data row5 col2\" >7000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow5_col4\" class=\"data row5 col4\" >30000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow5_col5\" class=\"data row5 col5\" >2082</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow5_col6\" class=\"data row5 col6\" >2.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow6_col0\" class=\"data row6 col0\" >trade</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow6_col1\" class=\"data row6 col1\" >15950</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow6_col2\" class=\"data row6 col2\" >5500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow6_col3\" class=\"data row6 col3\" >14500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow6_col4\" class=\"data row6 col4\" >45000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow6_col5\" class=\"data row6 col5\" >4419</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow6_col6\" class=\"data row6 col6\" >4.55%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow7_col0\" class=\"data row7 col0\" >logistic</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow7_col1\" class=\"data row7 col1\" >15948</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow7_col2\" class=\"data row7 col2\" >7000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow7_col3\" class=\"data row7 col3\" >13167</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow7_col4\" class=\"data row7 col4\" >37500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow7_col5\" class=\"data row7 col5\" >1130</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow7_col6\" class=\"data row7 col6\" >1.16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow8_col0\" class=\"data row8 col0\" >realestate</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow8_col1\" class=\"data row8 col1\" >15582</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow8_col2\" class=\"data row8 col2\" >5250</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow8_col3\" class=\"data row8 col3\" >13000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow8_col4\" class=\"data row8 col4\" >37500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow8_col5\" class=\"data row8 col5\" >1218</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow8_col6\" class=\"data row8 col6\" >1.26%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow9_col0\" class=\"data row9 col0\" >gov</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow9_col1\" class=\"data row9 col1\" >15168</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow9_col2\" class=\"data row9 col2\" >5114</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow9_col3\" class=\"data row9 col3\" >13500</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow9_col4\" class=\"data row9 col4\" >33257</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow9_col5\" class=\"data row9 col5\" >1271</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow9_col6\" class=\"data row9 col6\" >1.31%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow10_col0\" class=\"data row10 col0\" >energy</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow10_col1\" class=\"data row10 col1\" >14828</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow10_col2\" class=\"data row10 col2\" >3750</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow10_col3\" class=\"data row10 col3\" >14000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow10_col4\" class=\"data row10 col4\" >30000</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow10_col5\" class=\"data row10 col5\" >1324</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow10_col6\" class=\"data row10 col6\" >1.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow11_col0\" class=\"data row11 col0\" >ads</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow11_col1\" class=\"data row11 col1\" >14619</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow11_col2\" class=\"data row11 col2\" >5928</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow11_col3\" class=\"data row11 col3\" >13389</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow11_col4\" class=\"data row11 col4\" >29166</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow11_col5\" class=\"data row11 col5\" >991</td>\n",
       "                        <td id=\"T_d4c8fbdc_74f5_11ea_ad76_701ce71031efrow11_col6\" class=\"data row11 col6\" >1.02%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196a32b8b08>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
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